Machine translation refers to the automatic translation of text, using a computer system, for example, from one language (“a source language” or “a first language”) into another language (“a target language” or “a second language”). The internet features numerous machine translation systems available for a variety of languages. Such machine translation applications or programs often allow a user to translate an HTML page (that is, a web page, often accessed from a website) from one language into another language. After translation, the page is rendered in the target language as previously specified by the user. Examples of current, free-to-use machine translation systems that can be employed in this manner include Google® Translation and Babel Fish Translation at AltaVista®, both of which are partially powered by SYSTRAN® Language Translation Technologies (specifically SYSTRANBox). Examples of other current, commercial machine translation systems that can be employed in this manner include IBM WebSphere® Translation Server, SYSTRANLinks and SYSTRANet®.
FIG. 1 shows a conventional example of an output of a machine translation system. In FIG. 1, two web browser windows are open 2, 4 showing the same web page, a CNN® page titled “Unique—and tasty—stops for your next road trip,” in two different languages. The page is in English in the top browser window 2. Using Google® Translation, the text in the web page was translated from English into Spanish, with the results as shown in the bottom browser window 4. Other than the source language, target language and web page, no additional options or features are currently available with Google® Translation.
Machine translation technology usually falls into one of two major types. One type is rule-based, where an individual writes numerous rules, often numbering in the hundreds to thousands, to translate the source text from the source language into the target language. The quality of the resulting text depends on the robustness of the rules.
More recently, automatic methods have been used to induce rules or produce phrase libraries from parallel training corpora. Additional methods have been employed to compute the strength of the rules produced or the confidence in the translation method and tools (dictionaries, for example) employed. This type of machine translation is often referred to as statistical machine translation. Methods of generating the alignment between the source text and the result test and statistical methods of performing machine translation have been previously described in commonly-assigned U.S. Pat. No. 5,510,981 to Berger et al., “Language Translation Apparatus And Method Using Context-Based Translation Models.” Methods of performing phrase-based translation have been widely published. See, e.g., Franz Josef Och and Hermann Ney. “Statistical Machine Translation”. EAMT Workshop, pp. 39-46, Ljubljana, Slovenia, May 2000.
For each source phrase that consists of one or more source language words, a phrase library is consulted to obtain a set of target language sequences. Since different languages may have different word ordering patterns, a search is conducted over a window of source language positions to find the best translation. The process is repeated until the entire source sentence has been translated. Once the search is concluded, one can backtrack through the search hypotheses to find the best translation and alignment to the source language words.
Presently available machine translation systems generally have a limited user interface and/or are limited in the amount of information presented to a user.